A Data-Driven Multi-Model Methodology with Deep Feature Selection for Short-Term Wind Forecasting

Brian Hodge, Cong Feng, Mingjian Cui, Jie Zhang

Research output: Contribution to journalArticlepeer-review

268 Scopus Citations


With the growing wind penetration into the power system worldwide, improving wind power forecasting accuracy is becoming increasingly important to ensure continued economic and reliable power system operations. In this paper, a data-driven multi-model wind forecasting methodology is developed with a two-layer ensemble machine learning technique. The first layer is composed of multiple machine learning models that generate individual forecasts. A deep feature selection framework is developed to determine the most suitable inputs to the first layer machine learning models. Then, a blending algorithm is applied in the second layer to create an ensemble of the forecasts produced by first layer models and generate both deterministic and probabilistic forecasts. This two-layer model seeks to utilize the statistically different characteristics of each machine learning algorithm. A number of machine learning algorithms are selected and compared in both layers. This developed multi-model wind forecasting methodology is compared to several benchmarks. The effectiveness of the proposed methodology is evaluated to provide 1-hour-ahead wind speed forecasting at seven locations of the Surface Radiation network. Numerical results show that comparing to the single-algorithm models, the developed multi-model framework with deep feature selection procedure has improved the forecasting accuracy by up to 30%.

Original languageAmerican English
Pages (from-to)1245-1257
Number of pages13
JournalApplied Energy
StatePublished - 2017

Bibliographical note

Publisher Copyright:
© 2017 Elsevier Ltd

NREL Publication Number

  • NREL/JA-5000-67910


  • Data-driven
  • Ensemble forecasting
  • Feature selection
  • Machine learning
  • Multi-model
  • Wind forecasting


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